Skip to main content
View expanded cover

International Workshop on Algorithms in Bioinformatics

WABI 2015: Algorithms in Bioinformatics pp 1–15Cite as

BicNET: Efficient Biclustering of Biological Networks to Unravel Non-Trivial Modules

Part of the Lecture Notes in Computer Science book series (LNBI,volume 9289)

Abstract

The discovery of dense biclusters in biological networks received an increasing attention in recent years. However, despite the importance of understanding the cell behavior, dense biclusters can only identify modules where genes, proteins or metabolites are strongly connected. These modules are thus often associated with trivial, already known interactions or background processes not necessarily related with the studied conditions. Furthermore, despite the availability of biclustering algorithms able to discover modules with more flexible coherency, their application over large-scale biological networks is hampered by efficiency bottlenecks. In this work, we propose BicNET (Biclustering NETworks), an algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. First, we motivate the relevance of discovering network modules given by constant, symmetric and plaid biclustering models. Second, we propose a solution to discover these flexible modules without time and memory bottlenecks by seizing high efficiency gains from the inherent structural sparsity of networks. Results from the analysis of protein and gene interaction networks support the relevance and efficiency of BicNET.

Keywords

  • Bipartite Graph
  • Biological Network
  • Weighted Graph
  • Association Rule Mining
  • Frequent Itemset Mining

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-662-48221-6_1
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-662-48221-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Notes

  1. 1.

    Let \(\mathcal {L}\) be a finite set of items, and P an itemset \(P\subseteq \mathcal {L}\). A discrete matrix D is a set of transactions in \(\mathcal {L}\), \(\{P_1,..,P_n\}\). Let the coverage \(\varPhi _{P}\) of an itemset P be the set of transactions in D in which P occurs, \(\{P_i \in D\mid P\subseteq P_i\}\), and its support \(sup_P\) be the coverage size, \(\mid \) \(\varPhi _{P}\) \(\mid \). Given D and a minimum support \(\theta \), the frequent itemset mining task aims to compute: \(\{P \mid P \subseteq \mathcal {L}, sup_P \ge \theta \}\).

    Given D, let a matrix A be the concatenation of D elements with their column indexes. Let \(\varPsi _P\) of an itemset P in A be its indexes, and \(\varUpsilon _P\) be its original items in \(\mathcal {L}\). A set of biclusters \(\cup _k (I_k,J_k)\) can be derived from frequent itemsets \(\cup _k P_k\) by mapping \((I_k,J_k)\)=\((\varPhi _{P_k},\varPsi _{P_k})\) to compose constant biclusters with coherency across rows (\((I_k,J_k)\)=\((\varPsi _{P_k},\varPhi _{P_k})\) for column-coherency) with pattern \(\varUpsilon _P\).

  2. 2.

    Sparse prior equation with decreasing sparsity until able to retrieve a non-empty set of biclusters.

References

  1. Atluri, G., Bellay, J., Pandey, G., Myers, C., Kumar, V.: Discovering coherent value bicliques in genetic interaction data. In: IW on Data Mining in Bioinformatics (2010)

    Google Scholar 

  2. Barabasi, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5(2), 101–113 (2004)

    CrossRef  Google Scholar 

  3. Bellay, J., Atluri, G., Sing, T.L., Toufighi, K., Costanzo, M., et al.: Putting genetic interactions in context through a global modular decomposition. Genome Res. 21(8), 1375–1387 (2011)

    CrossRef  Google Scholar 

  4. Ben-Dor, A., Chor, B., Karp, R., Yakhini, Z.: Discovering local structure in gene expression data: the order-preserving submatrix problem. In: RECOMB, pp. 49–57. ACM (2002)

    Google Scholar 

  5. Berg, J., Lässig, M.: Local graph alignment and motif search in biological networks. Nat. Acad. Sci. 101(41), 14689–14694 (2004)

    CrossRef  Google Scholar 

  6. Chen, J., Yuan, B.: Detecting functional modules in the yeast protein protein interaction network. Bioinformatics 22(18), 2283–2290 (2006)

    CrossRef  Google Scholar 

  7. Cheng, Y., Church, G.: Biclustering of expression data. In: ISMB, pp. 93–103. AAAI (2000)

    Google Scholar 

  8. Colak, R.: Towards finding the complete modulome: density constrained biclustering. Ph.D. thesis, Simon Fraser University (2008)

    Google Scholar 

  9. Colak, R., Moser, F., Chu, J.S.C., Schönhuth, A., Chen, N., Ester, M.: Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks. PLoS One 5(10), e13348 (2010)

    CrossRef  Google Scholar 

  10. Dao, P., Colak, R., Salari, R., Moser, F., Davicioni, E., Schnhuth, A., Ester, M.: Inferring cancer subnetwork markers using density-constrained biclustering. Bioinformatics 26(18), i625–i631 (2010)

    CrossRef  Google Scholar 

  11. Ding, C., Zhang, Y., Li, T., Holbrook, S.: Biclustering protein complex interactions with a biclique finding algorithm. In: ICDM, pp. 178–187 (2006)

    Google Scholar 

  12. Georgii, E., Dietmann, S., Uno, T., Pagel, P., Tsuda, K.: Enumeration of condition-dependent dense modules in protein interaction networks. Bioinformatics 25(7), 933–940 (2009)

    CrossRef  Google Scholar 

  13. Henriques, R., Madeira, S.: Biclustering with flexible plaid models to unravel interactions between biological processes. IEEE/ACM TCBB (2015). doi:10.1109/TCBB.2014.2388206

  14. Henriques, R., Antunes, C., Madeira, S.C.: A structured view on pattern mining-based biclustering. Pattern Recognition (2015). http://www.sciencedirect.com/science/article/pii/S003132031500240X

  15. Henriques, R., Madeira, S.: Bicpam: pattern-based biclustering for biomedical data analysis. Algorithms Mol. Biol. 9(1), 27 (2014)

    CrossRef  Google Scholar 

  16. Henriques, R., Madeira, S.C.: Pattern-based biclustering with constraints for gene expression data analysis. In: 17th Portuguese Conference on Artificial Intelligence (EPIA-2015), Computational Methods in Bioinformatics and Systems Biology (CMBSB), Coimbra, Portugal. LNAI. Springer, Heidelberg (2015)

    Google Scholar 

  17. Henriques, R., Madeira, S.C., Antunes, C.: F2g: efficient discovery of full-patterns. In: ECML/PKDD IW on New Frontiers to Mine Complex Patterns. Springer-Verlag (2013)

    Google Scholar 

  18. Hochreiter, S., et al.: FABIA: factor analysis for bicluster acquisition. Bioinformatics 26(12), 1520–1527 (2010)

    CrossRef  Google Scholar 

  19. Ideker, T., Ozier, O., Schwikowski, B., Siegel, A.F.: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(suppl 1), S233–S240 (2002)

    CrossRef  Google Scholar 

  20. Ihmels, J., Bergmann, S., Barkai, N.: Defining transcription modules using large-scale gene expression data. Bioinformatics 20(13), 1993–2003 (2004)

    CrossRef  Google Scholar 

  21. Koh, J.L.Y., Ding, H., Costanzo, M., Baryshnikova, A., Toufighi, K., Bader, G.D., Myers, C.L., Andrews, B.J., Boone, C.: Drygin: a database of quantitative genetic interaction networks in yeast. Nucleic Acids Res. 38(suppl 1), D502–D507 (2010)

    CrossRef  Google Scholar 

  22. MacPherson, J.I., Dickerson, J., Pinney, J., Robertson, D.: Patterns of HIV-1 protein interaction identify perturbed host-cellular subsystems. PLoS Comput. Biol. 6(7), e1000863 (2010)

    CrossRef  Google Scholar 

  23. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM TCBB 1(1), 24–45 (2004)

    Google Scholar 

  24. Maulik, U., Mukhopadhyay, A., Bhattacharyya, M., Kaderali, L., Brors, B., Bandyopadhyay, S., Eils, R.: Mining quasi-bicliques from HIV-1-human protein interaction network: a multiobjective biclustering approach. IEEE/ACM TCBB 10(2), 423–435 (2013)

    Google Scholar 

  25. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S.: A novel biclustering approach to association rule mining for predicting HIV-1 human protein interactions. PLoS ONE 7(4), e32289 (2012)

    CrossRef  Google Scholar 

  26. Murali, T.M., Kasif, S.: Extracting conserved gene expression motifs from gene expression data. Pac. Symp. Biocomput. 8, 77–88 (2003)

    Google Scholar 

  27. Pereira-Leal, J.B., Enright, A.J., Ouzounis, C.A.: Detection of functional modules from protein interaction networks. Proteins Struct. Funct. Bioinf. 54(1), 49–57 (2004)

    CrossRef  Google Scholar 

  28. Segal, E., Wang, H., Koller, D.: Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19(suppl 1), i264–i272 (2003)

    CrossRef  Google Scholar 

  29. Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Mol. Syst. Biol. 3(1), 88 (2007)

    Google Scholar 

  30. Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Natl. Acad. Sci. 100(21), 12123–12128 (2003)

    CrossRef  Google Scholar 

  31. Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K.P., et al.: String v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2014). p.gku1003

    CrossRef  Google Scholar 

  32. Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18, 136–144 (2002)

    CrossRef  Google Scholar 

  33. Tomaino, V., Guzzi, P.H., Cannataro, M., Veltri, P.: Experimental comparison of biclustering algorithms for PPI networks. In: BCB, pp. 671–676. ACM (2010)

    Google Scholar 

  34. Xiong, H., Heb, X.F., Ding, C., Zhang, Y., Kumar, V., Holbrook, S.R.: Identification of functional modules in protein complexes via hyperclique pattern discovery. Pac. Symp. Biocomput. 10, 221–232 (2005)

    Google Scholar 

Download references

Acknowledgments

This work was supported by FCT under the project UID/CEC/ 50021/2013 and the PhD grant SFRH/BD/75924/2011 to RH.

Author information

Authors and Affiliations

  1. Inesc-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Rui Henriques & Sara C. Madeira

Authors
  1. Rui Henriques
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sara C. Madeira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rui Henriques .

Editor information

Editors and Affiliations

  1. University of Maryland, College Park, Maryland, USA

    Mihai Pop

  2. University of Lille, Lille, France

    Hélène Touzet

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Henriques, R., Madeira, S.C. (2015). BicNET: Efficient Biclustering of Biological Networks to Unravel Non-Trivial Modules. In: Pop, M., Touzet, H. (eds) Algorithms in Bioinformatics. WABI 2015. Lecture Notes in Computer Science(), vol 9289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48221-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-48221-6_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48220-9

  • Online ISBN: 978-3-662-48221-6

  • eBook Packages: Computer ScienceComputer Science (R0)